This study condenses huge amount of raw data measured from a MEMS accelerometer-based, wrist-worn device on different levels of physical activities (PAs) for subjects wearing the device 24 h a day continuously. In this study, we have employed the device to build up assessment models for quantifying activities, to develop an algorithm for sleep duration detection and to assess the regularity of activity of daily living (ADL) quantitatively. A new parameter, the activity index (AI), has been proposed to represent the quantity of activities and can be used to categorize different PAs into 5 levels, namely, rest/sleep, sedentary, light, moderate, and vigorous activity states. Another new parameter, the regularity index (RI), was calculated to represent the degree of regularity for ADL. The methods proposed in this study have been used to monitor a subject’s daily PA status and to access sleep quality, along with the quantitative assessment of the regularity of activity of daily living (ADL) with the 24-h continuously recorded data over several months to develop activity-based evaluation models for different medical-care applications. This work provides simple models for activity monitoring based on the accelerometer-based, wrist-worn device without trying to identify the details of types of activity and that are suitable for further applications combined with cloud computing services.

Chronic obstructive pulmonary disease (COPD) claimed 3.0 million lives in 2016 and ranked 3rd among the top 10 global causes of death. Moreover, once diagnosed and discharged from the hospital, the 30-day readmission risk in COPD patients is found to be the highest among all chronic diseases. The existing diagnosis methods, such as Global Initiative for Chronic Obstructive Lung Disease (GOLD) 2019, Body-mass index, airflow Obstruction, Dyspnea, and Exercise (BODE) index, modified Medical Research Council (mMRC), COPD assessment test (CAT), 6-minute walking distance, which are adopted currently by physicians cannot predict the potential readmission of COPD patients, especially within the 30 days after discharge from the hospital. In this paper, a statistical model was proposed to predict the readmission risk of COPD patients within 30-days by monitoring their physical activity (PA) in daily living with accelerometer-based wrist-worn wearable devices. This proposed model was based on our previously reported PA models for activity index (AI) and regularity index (RI) and it introduced a new parameter, quality of activity (QoA), which incorporates previously proposed parameters, such as AI and RI, with other activity-based indices to predict the readmission risk. Data were collected from continuous PA monitoring of 16 COPD patients after hospital discharge as test subjects and readmission prediction criteria were proposed, with a 63% sensitivity and a 37.78% positive prediction rate. Compared to other clinical assessment, diagnosis, and prevention methods, the proposed model showed significant improvement in predicting the 30-day readmission risk.

Chronic obstructive pulmonary disease (COPD) is a significantly concerning disease, and is ranked highest in terms of 30-day hospital readmission. Generally, physical activity (PA) of daily living reflects the health status and is proposed as a strong indicator of 30-day hospital readmission for patients with COPD. This study attempted to predict 30-day hospital readmission by analyzing continuous PA data using machine learning (ML) methods. Data were collected from 16 patients with COPD over 3877 days, and clinical information extracted from the patients’ hospital records. Activity-based parameters were conceptualized and evaluated, and ML models were trained and validated to retrospectively analyze the PA data, identify the nonlinear classification characteristics of different risk factors, and predict hospital readmissions. Overall, this study predicted 30-day hospital readmission and prediction performance is summarized as two distinct approaches: prediction-based performance and event-based performance. In a prediction-based performance analysis, readmissions predicted with 70.35% accuracy; and in an event-based performance analysis, the total 30-day readmissions were predicted with a precision of 72.73%. PA data reflect the health status; thus, PA data can be used to predict hospital readmissions. Predicting readmissions will improve patient care, reduce the burden of medical costs burden, and can assist in staging suitable interventions, such as promoting PA, alternate treatment plans, or changes in lifestyle to prevent readmissions.

Background: Aim of this study was to validate thoracic trauma severity score (TTSS) in assessing the requirement of mechanical ventilation, mortality, and predicting prognosis in chest injury patients.Methods: This study was conducted in department of general surgery, Dr. S. N. Medical College, Jodhpur, Rajasthan, from December 2018 to September 2019. This was a single centred, prospective, observational study, conducted in 110 patients, aged >18 years, of isolated chest injury, excluding polytrauma patients. Data was summarized in the form of proportions, histograms and tables to show relationships of parameters with results. Data was presented as mean±SD and proportions as appropriate. Chi square test, z test or t tests were used wherever necessary for association analysis between categorical variables. Diagnostic test characteristics for mortality and complications was calculated from the ROC curves. A two sided of p values of less than 0.05 was considered statistically significant.Results: Most common mode of chest injury was blunt trauma and most common age group affected was 42-54 years. Maximum mortality was seen in TTSS between 16-20, shows higher the TTSS more the mortality. Ventilator requirement was more in high TTSS. Patients with higher TTSS had longer hospital stay as compared to patients with lower TTSS.Conclusions: On application of TTSS on admission, TTSS had direct correlation with need for oxygenation, ventilator need, duration of hospital stay, mortality or outcome in chest trauma patients. Thus we recommend TTSS as a good useful score for evaluation of prognosis, outcome and mortality in chest trauma patients.

Machine learning (ML) based prediction models proved to be fast, accurate, and free from human errors with capabilities to address pressing problems in healthcare. Being progressive in nature, Chronic Obstructive Pulmonary Disease (COPD) patients require frequent hospital readmission. Frequent hospital readmission, may be preventable, is a patient-centric approach which result in expensive health services and poor utilization of overly-burdened medical resources in recent times. In this research study, we envisaged a ML-based model to predict hospital readmission in 30-day by analyzing daily physical activity (PA) data with an accelerometer-based wrist-worn device. Prediction models based on logistic regression, lasso regularization, and MLP deep neural network have been used for training and testing PA data. For analysis, 70% of PA data used for training and 30% for testing and predicting readmission. Readmission predicted with sensitivity 0.88, positive predictive value 0.75, false positive 0.25 and area under ROC curve 0.50. The novelty our approach is to predict hospital readmission by monitoring health condition with accelerometer-based wrist-worn device which generate PA data electronic in nature, can be readily employed with various e-healthcare services. Therefore, we propose to develop a cloud-based system, coordinated & aligned with local-health services and e-healthcare, to provide better patient care system and alarming notifications; and to reduce preventable hospital readmission to cut medical expenses and conserve medical resources for better patient care and readily available medical services for all.

In this study, a model for identifying the levels of physical activity (PA) with a wrist-worn accelerometer-based device has been proposed. The levels of identified PA have been categorized into rest/sleep, sedentary, light, moderate, and vigorous activity states by analyzing the data collected from 10 normal subjects. An activity-based sleep duration detection algorithm has been proposed and implemented thereafter to further distinguish activities between short period of rest and sleep. The model and method proposed in this study could be further used to monitor subject's daily PA status and sleep quality assessment in the future for various home-based healthcare applications.